ontolearn.learners.spell

SPELL Learner - SAT-based concept learning using SPELL fitting.

Classes

SPELL

SPELL: SAT-based concept learner using general SPELL fitting.

Module Contents

class ontolearn.learners.spell.SPELL(knowledge_base: AbstractKnowledgeBase, reasoner: owlapy.abstracts.AbstractOWLReasoner | None = None, max_runtime: int | None = 60, max_query_size: int = 10, starting_query_size: int = 1, search_mode: str = 'full_approx')[source]

Bases: ontolearn.learners.sat_base.SATBaseLearner

SPELL: SAT-based concept learner using general SPELL fitting.

This learner uses SAT solvers to find concept expressions that fit positive and negative examples. Unlike ALCSAT which is specialized for ALC, SPELL uses the more general fitting.py module which supports different modes of operation.

The algorithm incrementally searches for queries of increasing size that maximize the coverage on the given examples.

kb

The knowledge base that the concept learner is using.

Type:: AbstractKnowledgeBase

max_query_size

Maximum size of queries to search for.

Type:: int

search_mode: Search mode - exact, neg_approx, or full_approx.

_best_hypothesis

Best found hypothesis.

Type:: OWLClassExpression

_best_hypothesis_accuracy

Accuracy of the best hypothesis.

Type:: float

_structure

Internal structure representation of the knowledge base.

Type:: Structure

_ind_to_owl

Mapping from internal individual indices to OWL individuals.

Type:: dict

_owl_to_ind

Mapping from OWL individuals to internal indices.

Type:: dict

__slots__ = ('max_query_size', 'starting_query_size', 'search_mode')

name = 'spell'

max_query_size = 10

starting_query_size = 1

search_mode

fit(lp: PosNegLPStandard)[source]

Find concept expressions that explain positive and negative examples.

Parameters:: lp – Learning problem with positive and negative examples.
Returns:: self